Integrated design method and system for three-dimensional simulation model of lamp equipment

Abstract

The invention relates to the technical field of lamp model integration, in particular to an integrated design method and system for a three-dimensional simulation model of lamp equipment. The method comprises the following steps: acquiring lamp equipment basic data, classifying the lamp equipment basic data according to components, performing three-dimensional reconstruction of independent components, forming three-dimensional lamp basic composition units, acquiring a physical connection relationship, determining functional positioning of each unit, and segmenting the lamp equipment basic data into three-dimensional decoration units and three-dimensional light source units; determining a physical support structure according to the three-dimensional decoration unit; simulating and optimizing a load bearing design through weight aggravation; generating and optimizing the three-dimensional decoration unit; performing light scattering simulation based on the three-dimensional light source unit, tracking a light scattering track of the light source, optimizing the three-dimensional decoration unit according to the light scattering track, and integrating the three-dimensional light source unit and the final decoration unit. And forming a lamp equipment design integration model. According to the invention, the design efficiency of the lamp equipment is improved, and the digitization and intellectualization process in the field of lamp design is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of lamp model integration, and in particular to an integrated design method and system for a three-dimensional simulation model of a lamp device. Background Art

[0002] Traditional integrated design methods for three-dimensional simulation models of lighting equipment often rely on manual drawing and empirical judgment, resulting in low design efficiency and lack of precision. Especially in complex lighting structures, the physical connection relationship between components is difficult to accurately grasp, and a systematic design approach cannot be achieved. Traditional three-dimensional modeling technology has obvious limitations in processing dynamic lighting effects and structural optimization, and cannot meet the modern market's demand for high-efficiency and high-aesthetics lamps. In addition, existing technologies rely on simple theoretical models for light scattering simulation, lack in-depth analysis of real light propagation behavior, and cannot provide sufficient data support to ensure the lighting efficiency and visual effects of lamps. The load-bearing design of lamps usually lacks scientific basis, which may lead to safety hazards in actual applications. Especially in large-scale lamps and commercial lighting scenarios, designers face multiple challenges and find it difficult to take into account both aesthetics and functionality. Summary of the Invention

[0003] Based on this, it is necessary to provide an integrated design method and system for a three-dimensional simulation model of a lighting device to solve at least one of the above technical problems.

[0004] To achieve the above objectives, an integrated design method for a three-dimensional simulation model of a lighting device includes the following steps:

[0005] Step S1: Obtain basic data of the lighting equipment; classify the basic data of the lighting equipment according to each component to obtain data of each component; perform three-dimensional reconstruction of the independent components based on the data of each component to obtain a three-dimensional lighting basic component unit of each component;

[0006] Step S2: obtaining the physical connection relationship of the lighting equipment; determining the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; dividing the three-dimensional lighting basic component unit into a three-dimensional decoration unit and a three-dimensional light source unit based on the functional positioning of each unit;

[0007] Step S3: confirming the physical support structure of the lamp based on the three-dimensional decorative unit; performing a weight-intensified simulation using basic data of the lamp equipment, and optimizing the load-bearing margin of the physical support structure of the lamp based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit;

[0008] Step S4: performing a light scattering simulation of the lamp based on the three-dimensional light source unit to generate simulated light scattering data; and tracing the light scattering trajectory of the light source based on the simulated light scattering data;

[0009] Step S5: optimizing the light source influence on the optimized three-dimensional decoration unit according to the light source light scattering trajectory, thereby generating a final three-dimensional decoration unit; integrating the three-dimensional light source unit and the final three-dimensional decoration unit to obtain a lighting equipment design integrated model.

[0010] The present invention realizes the systematic classification of each component by acquiring the basic data of the lamp equipment, ensuring the efficient management and processing of the data. The three-dimensional reconstruction of the independent components makes the basic component units of the lamp visible, providing an intuitive basis for the subsequent design. The acquisition of the physical connection relationship determines the functional positioning of each unit, ensuring the scientificity and rationality of the design. The division of the three-dimensional decorative unit and the three-dimensional light source unit lays a structural foundation for the subsequent optimization design. The process of confirming the physical support structure of the lamp is simulated by weight intensification, providing an in-depth analysis of the support structure. The implementation of the optimized design ensures the safety and stability of the lamp. The generated optimized three-dimensional decorative unit improves the aesthetics and functionality of the overall design. The light scattering simulation provides real data support for the light efficiency evaluation of lamps. The generation and tracking of simulated light scattering data realizes a comprehensive understanding of light source performance. The implementation of light source impact optimization ensures the optical effect of the final decorative unit in actual application. The integrated three-dimensional light source unit and the final three-dimensional decorative unit form an integrated lighting equipment design model, which not only improves the integrity and consistency of the design, but also provides a detailed technical basis for subsequent production and application. The implementation of the overall method improves the design efficiency of lighting equipment, shortens the development cycle, reduces costs, and promotes the digitalization and intelligentization of the lighting design field, meets the modern market demand for high-quality lamps, and enhances the market competitiveness and innovation capabilities of lighting products.

[0011] The present invention further provides an integrated design system for a three-dimensional simulation model of a lighting device, which is used to execute the integrated design method for a three-dimensional simulation model of a lighting device as described above. The integrated design system for a three-dimensional simulation model of a lighting device includes:

[0012] The component modeling module is used to obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each component; and perform three-dimensional reconstruction of independent components based on the data of each component to obtain the three-dimensional basic lighting unit of each component;

[0013] A connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into a three-dimensional decorative unit and a three-dimensional light source unit based on the functional positioning of each unit;

[0014] The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit. It performs a weight-intensification simulation based on the basic data of the lamp equipment and optimizes the load-bearing margin of the lamp's physical support structure based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit.

[0015] A light simulation module is used to simulate the light scattering of lamps based on a three-dimensional light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data;

[0016] The fusion integration module is used to optimize the light source influence on the optimized three-dimensional decorative unit according to the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; integrating the three-dimensional light source unit and the final three-dimensional decorative unit to obtain an integrated model of lighting equipment design.

[0017] The present invention realizes the efficient acquisition and classification of basic data of lamp equipment through the implementation of component modeling module, ensures the systematicness and integrity of various component data, and the three-dimensional reconstruction of independent components provides intuitive visual expression, which is convenient for designers to conduct in-depth analysis of lamp structure. The introduction of connection identification module ensures that the physical connection relationship between the components of the lamp is accurately identified, thereby clearly defining the functional positioning of each unit. The division of three-dimensional decorative unit and three-dimensional light source unit provides a clear structural basis for subsequent design. The structural optimization module optimizes the load-bearing margin design of the physical support structure of the lamp through weight intensification simulation, ensuring the safety and stability of the lamp. The generated optimized three-dimensional decorative unit is not only improved in load-bearing capacity, but also enhances the overall aesthetics and practicality. The application of light simulation module simulates light scattering of three-dimensional light source unit. The generated simulated light scattering data provides an important empirical basis for the optical performance of the lamp. The process of tracing the light scattering trajectory of the light source provides accurate data support for the subsequent optimization of the light source influence. The implementation of the fusion integration module realizes the effective combination of optimizing the three-dimensional decorative unit and the light source influence. The final three-dimensional decorative unit generated improves the light efficiency and lighting effect of the lamp. The design integration model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only ensures the consistency and coordination of the overall design, but also provides a detailed technical basis for actual production. The realization of the overall system improves the efficiency of lamp design, shortens the development cycle, reduces production costs, and at the same time promotes the digitalization and intelligentization of lamp design, meets the modern market demand for high-quality lamps, improves the market competitiveness and innovation ability of products, and injects new vitality and motivation into the development of the lamp industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic flow chart of the steps of an integrated design method for a three-dimensional simulation model of a lighting device;

[0019] Figure 2 Detailed implementation flow chart of step S2;

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0023] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0024] To achieve this, please refer to Figures 1 to 2 , an integrated design method for a three-dimensional simulation model of a lighting device, comprising the following steps:

[0025] Step S1: Obtain basic data of the lighting equipment; classify the basic data of the lighting equipment according to each component to obtain data of each component; perform three-dimensional reconstruction of the independent components based on the data of each component to obtain a three-dimensional lighting basic component unit of each component;

[0026] Step S2: obtaining the physical connection relationship of the lighting equipment; determining the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; dividing the three-dimensional lighting basic component unit into a three-dimensional decoration unit and a three-dimensional light source unit based on the functional positioning of each unit;

[0027] Step S3: confirming the physical support structure of the lamp based on the three-dimensional decorative unit; performing a weight-intensified simulation using basic data of the lamp equipment, and optimizing the load-bearing margin of the physical support structure of the lamp based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit;

[0028] Step S4: performing a light scattering simulation of the lamp based on the three-dimensional light source unit to generate simulated light scattering data; and tracing the light scattering trajectory of the light source based on the simulated light scattering data;

[0029] Step S5: optimizing the light source influence on the optimized three-dimensional decoration unit according to the light source light scattering trajectory, thereby generating a final three-dimensional decoration unit; integrating the three-dimensional light source unit and the final three-dimensional decoration unit to obtain a lighting equipment design integrated model.

[0030] The present invention realizes the systematic classification of each component by acquiring the basic data of the lamp equipment, ensuring the efficient management and processing of the data. The three-dimensional reconstruction of the independent components makes the basic component units of the lamp visible, providing an intuitive basis for the subsequent design. The acquisition of the physical connection relationship determines the functional positioning of each unit, ensuring the scientificity and rationality of the design. The division of the three-dimensional decorative unit and the three-dimensional light source unit lays a structural foundation for the subsequent optimization design. The process of confirming the physical support structure of the lamp is simulated by weight intensification, providing an in-depth analysis of the support structure. The implementation of the optimized design ensures the safety and stability of the lamp. The generated optimized three-dimensional decorative unit improves the aesthetics and functionality of the overall design. The light scattering simulation provides real data support for the light efficiency evaluation of lamps. The generation and tracking of simulated light scattering data realizes a comprehensive understanding of light source performance. The implementation of light source impact optimization ensures the optical effect of the final decorative unit in actual application. The integrated three-dimensional light source unit and the final three-dimensional decorative unit form an integrated lighting equipment design model, which not only improves the integrity and consistency of the design, but also provides a detailed technical basis for subsequent production and application. The implementation of the overall method improves the design efficiency of lighting equipment, shortens the development cycle, reduces costs, and promotes the digitalization and intelligentization of the lighting design field, meets the modern market demand for high-quality lamps, and enhances the market competitiveness and innovation capabilities of lighting products.

[0031] In an embodiment of the present invention, the integrated design method for a three-dimensional simulation model of a lighting device includes the following steps:

[0032] Step S1: Obtain basic data of the lighting equipment; classify the basic data of the lighting equipment according to each component to obtain data of each component; perform three-dimensional reconstruction of the independent components based on the data of each component to obtain a three-dimensional lighting basic component unit of each component;

[0033] In this embodiment, the operation of obtaining basic data of the lighting equipment adopts a structured acquisition method. Data is extracted from the lighting design drawings, product modeling documents, and manufacturing parameter files. Vector recognition is performed on the three-view drawings using an image recognition system to identify the size, shape, boundary, and relative position relationship of each lighting component. The geometric construction lines and annotated line segments in the drawings are identified using OpenCV image processing tools and converted into numerical structural information. The triangular mesh data is then parsed using the STL format part model file to extract the basic point matrix of the three-dimensional component shape. After all data is uniformly converted into structural feature data in a unified coordinate system, the data is divided into multiple component categories such as lampshades, lamp bases, heat sinks, fasteners, lenses, and light source mounting structures according to the lamp body structure classification standard. A data set is created for each component category. In the 3D reconstruction process, a voxel splicing reconstruction method based on point cloud density distribution control is used to reconstruct the volumetric configuration of the structural data of each component category. The MeshLab tool is used to perform voxel fitting processing. The 3D geometric model of each component is generated by edge slicing scanning to serve as the basic component unit of the 3D lighting.

[0034] Step S2: obtaining the physical connection relationship of the lighting equipment; determining the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; dividing the three-dimensional lighting basic component unit into a three-dimensional decoration unit and a three-dimensional light source unit based on the functional positioning of each unit;

[0035] In this embodiment, the process of obtaining the physical connection relationship of the lighting equipment is analyzed by scanning the part assembly page information in the manufacturing drawing, and the physical assembly path between the components is established by using the primitive cascade connection tree method. The parent-child structure pairs in the BOM (Bill of Materials) structure table are used to identify the relationship between the connecting parts and the connected parts, and then the physical connection positioning is performed by the connection node extraction method. Through these connection information, each component in the aforementioned three-dimensional lighting basic component unit is established with the object connected to it. The functional area of ​​the three-dimensional lighting basic component unit is positioned by using the spatial constraint analysis method. By calculating the gravity centerline direction and wire penetration direction between the connection component pairs in the three-dimensional coordinates, the physical function of each component in the structure is inferred, and then the component unit that assumes the support structure function is marked as a three-dimensional decorative unit, and the component unit with embedded light source or light guide structure is marked as a three-dimensional light source unit. The structural function partition matrix identification method is used to complete the marking operation for all units.

[0036] Step S3: confirming the physical support structure of the lamp based on the three-dimensional decorative unit; performing a weight-intensified simulation using basic data of the lamp equipment, and optimizing the load-bearing margin of the physical support structure of the lamp based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit;

[0037] In this embodiment, the operation of confirming the physical support structure of the lamp based on the three-dimensional decorative unit is reconstructed based on the three-dimensional volume distribution of the decorative unit and the relationship between the contact area of ​​the components. Spatial collision detection is performed through the CAD structural analysis plug-in to identify the support path with the largest contact area, and the component with the maximum axial stability is selected in the support path area to establish a physical support skeleton. Subsequently, in the load-bearing simulation link, the material density parameters and the geometric structure parameters of each unit contained in the basic data of the lamp equipment are input into the finite element analysis platform to construct a complete mechanical simulation grid model, and the self-weight aggravation factor is set to 1.25. The self-weight load superposition simulation is performed within the spatial posture range of the lamp to obtain the stress distribution diagram of the support structure, and then the cross-sectional size is adjusted based on the stress peak area in the diagram. The Beamshell hybrid modeling method is used to adjust the ratio of the surface thickness of the decorative unit and the hollow layer skeleton, thereby outputting the optimized structural configuration of the three-dimensional decorative unit.

[0038] Step S4: performing a light scattering simulation of the lamp based on the three-dimensional light source unit to generate simulated light scattering data; and tracing the light scattering trajectory of the light source based on the simulated light scattering data;

[0039] In this embodiment, the process of simulating the light scattering of lamps based on three-dimensional light source units is implemented using a ray tracing algorithm. The three-dimensional light source units are input into a light simulation platform built based on the OpenGL framework. The radiation angle, wavelength range and initial light intensity of each light source unit are set. According to the actual parameters of the LED light source chip, the wavelength range is set to 380 nanometers to 780 nanometers, and the initial emission intensity is set to 1500 lumens. The light source light is emitted and passes through the current structural space at a particle tracing frequency of once per nanosecond. The collision positions, reflection angles and refractive coefficients of all light rays and surfaces are collected in three-dimensional space and recorded as simulated light scattering data. Light path labels are labeled for different light types in the simulated light scattering data. The main scattering paths emitted by all light sources are traced based on the longest path priority rule to form a complete light source light scattering trajectory, which is output as a three-dimensional vector data sequence.

[0040] Step S5: optimizing the light source influence on the optimized three-dimensional decoration unit according to the light source light scattering trajectory, thereby generating a final three-dimensional decoration unit; integrating the three-dimensional light source unit and the final three-dimensional decoration unit to obtain a lighting equipment design integrated model.

[0041] In this embodiment, the process of optimizing the light source influence of the optimized three-dimensional decorative unit according to the scattering trajectory of the light source is carried out by introducing the light interference convolution kernel function to perform spatial influence layering processing, extracting the terminal position of the scattered light in the aforementioned scattering trajectory and matching it with the surface grid points of the optimized three-dimensional decorative unit, calculating the unit light acceptance rate of each surface point, and applying the reverse grid weighting technology in the three-dimensional modeling platform to fine-tune the geometric structure of the grid area with a light acceptance rate higher than 85%, increasing the structural curvature to 0.45 to enhance the surface scattering and guiding capability, performing local excavation and cutting operations on the area with an acceptance rate lower than 15%, and changing the angle to a negative curvature structure to increase the reflection path density, then adjusting the structural thickness of the light-transmitting area by an order of 0.2 mm to improve the light transmittance coefficient, and completing the output of the three-dimensional decorative unit with optimized light source influence, and finally performing a Boolean operation to structurally integrate the three-dimensional light source unit and the final three-dimensional decorative unit to obtain an integrated model of the lighting equipment design.

[0042] Preferably, step S1 includes the following steps:

[0043] Step S11: Acquire basic data of lighting equipment; perform multi-dimensional attribute annotation on the basic data of lighting equipment, and deconstruct the basic data of lighting equipment into structural hierarchies based on the attribute annotation data, thereby obtaining a component hierarchical relationship;

[0044] Step S12: Mapping the component hierarchical relationship to the basic data of the lighting device, and classifying the basic data of the lighting device into component categories according to the component hierarchical relationship to obtain various types of component data, wherein the number of component categories is no less than four, including housing components, light source components, power supply components, and control components;

[0045] Step S13: Perform three-dimensional projection of each classification using each component data to obtain a component framework of each classification, wherein the three-dimensional projection uses 0.1-1.0mm voxel accuracy for contour reconstruction;

[0046] Step S14: Based on the component data, the component frames of each category are assembled into three-dimensional units to generate the three-dimensional lamp basic components of each component. The assembly size tolerance range is controlled within 0.3mm, and the component volume range is limited to 20-800cm 3 between.

[0047] In this embodiment, a three-dimensional scanner and a multimodal sensor array system are used to perform full-structure scanning and acquisition of the lighting equipment, wherein the three-dimensional scanner must have a data acquisition rate of at least 1 million points per second and a scanning accuracy of not less than 0.05 mm. During scanning, it must rotate 360 ​​degrees around the lighting equipment for uniform coverage to ensure that there is no data occlusion area. At the same time, an additional high-resolution industrial camera is used to record texture images and establish an RGB-depth mapping table. The collected raw point cloud data is preprocessed, including denoising, alignment and resampling operations. Subsequently, the point cloud is preliminarily segmented based on the component recognition algorithm, and combined with the infrared and contact electromagnetic property information obtained by the sensor, multi-dimensional attribute labeling operations such as size, material, weight, and electrical interface are applied to each component. The attribute labeling is organized in the form of key-value pairs and stored as structured tuple data. By combining the labeled data with the geometric topological relationship of the components, a graph construction method is used to establish a component hierarchical structure diagram. The structure diagram contains at least three or more levels and clearly defines each component. The connection boundaries and assembly positions between them are obtained, thereby obtaining the component hierarchical relationship dataset after the structural hierarchy is deconstructed. By establishing a mapping function, each level of component relationship is mapped to a specific spatial area in the original point cloud or mesh data. The mapping function used is constructed based on the weighted spatial Euclidean distance, topological continuity and attribute similarity. A unique identification code is set for each main component and embedded in the metadata index. According to the master-slave structure identified in the component hierarchical relationship, the index is called to classify and aggregate the original data. The aggregation process does not use global clustering but adopts a partitioning strategy based on conditional restrictions. Each type of component data is divided into shell components, light source components, power components and control components according to functional categories. Data overlap is not allowed. For each type of component, an independent data set and topology information table are established. The data set is used to store original content such as three-dimensional points, textures, and attributes. The topology information table is used to describe the positioning matrix and orientation vector of each type of component in space. Based on the original point cloud of the component, Marching is called The Cubes algorithm reconstructs voxel contours, setting the voxel unit side length between 0.1 and 1.0 mm. The voxel density is dynamically adjusted according to the complexity of the component's outer contour. If the component's outer shape has a large number of curved surface areas, the side length is compressed to 0.1 mm to ensure the smoothness of the curvature transition, the reconstructed contour data is generated by polygon meshing algorithm to generate triangular mesh bodies, and independent three-dimensional frameworks are established for the four types of components. The coordinate system used in the projection process is fixed to the right-hand coordinate system. The component framework after meshing must meet the continuity test, including mesh closure, boundary consistency, and topological no redundant points. The reconstruction result is stored as an OBJ file with an embedded attribute table to retain the original component annotation information. The corresponding transformation matrix is ​​called for each component to perform coordinate transformation and unify it to the global assembly coordinate system. The assembly operation needs to determine the assembly reference point and alignment surface according to the original lighting structure drawing or 3D CAD assembly constraints. The specific operation is performed through an assembly plug-in such as CATIA's Assembly The design module is used for this process, where each assembly contact surface must satisfy three-point coplanarity constraints and two-vector collinearity constraints to avoid redundant degrees of freedom. The assembly dimension tolerance is controlled within 0.3 mm. Inspection is performed by scanning and reconstructing the model after assembly and calculating the minimum distance between assembly surfaces. The assembly process must ensure that the component volume is controlled between 20 and 800 cubic centimeters. The size and constraint boundaries of the component's external envelope are determined using the Brep (boundary representation) analysis function. If the volume or boundary is found to be outside the specified range, the component is rejected and recorded as an abnormal unit. This completes the assembly and construction of the basic components of the 3D lamp, and the components are uniformly saved as a combined model data structure containing multiple data tags such as topology, attributes, and structural hierarchy.

[0048] Preferably, step S2 includes the following steps:

[0049] Step S21: Acquire the physical connection relationship of the lighting equipment; confirm the function of each connection point based on the physical connection relationship of the lighting equipment;

[0050] Step S22: Virtually projecting the three-dimensional lamp basic component unit onto each lamp device point in the physical connection relationship of the lamp device, wherein the projection range is: projection angle less than 30°, 30° to 60°, and 60° to 90°, and the projection accuracy requires an error range of less than 1 mm;

[0051] Step S23: mapping each lighting fixture point based on the functional role of each connection point, thereby obtaining the functional positioning of each unit in the three-dimensional lighting basic component unit;

[0052] Step S24: Based on the functional positioning of each unit, the basic unit of the lighting device is spatially divided to obtain a three-dimensional decorative unit and a three-dimensional light source unit, wherein the volume range of each unit is 1-1000cm 3 , the volume of the three-dimensional light source unit is 10-500cm 3 .

[0053] In this embodiment, a structural sensing matrix and an electromagnetic induction array are used to jointly collect the connection structure information of the lighting equipment. The structural sensing matrix is ​​arranged at the connection interface position of each component, and the structural constraint boundary of the connection part is collected through the stress response signal. The electromagnetic induction array is used to sense the electrical signal transmission path and contact connection structure between the components. The collected data includes the center coordinates of the connection surface, the normal vector, the connection type code, the electrical contact status and the fastening strength level. The collection accuracy requires that the spatial coordinate error does not exceed 0.3 mm. The electrical signal recognition frequency range is set to 1kHz to 20kHz, which is used to distinguish the high and low frequency connection states of the control component and the power component. The connection data is then structured through a graph neural network model based on point group and structural topology analysis. Processing, identify the function of each connection point, including six functional labels: power input, electrical signal control, neutral connection, mechanical fixation, heat dissipation conduction and decorative connection. By assigning a function vector to each connection point, the function confirmation process is completed. A unified assembly space scene is constructed using the virtual coordinate mapping module. The three-dimensional component unit is initially placed in the origin of the world coordinate system, and the cone projection simulation is performed on each physical connection point in turn. The projection angle range is set to less than 30 degrees, 30 degrees to 60 degrees, and 60 degrees to 90 degrees. Three cone models are generated, and three sets of virtual projection paths are generated respectively. Each cone model uses the connection point as the projection target endpoint and the center of the component unit as the projection source point. A direction vector field is established, and the affine transformation and vector rotation matrix are used to calculate each The rotation angle and displacement distance of the component during the projection process, and the Euclidean distance error between the component unit and the target point are measured in real time during each projection process to ensure that the error range is less than 1 mm. If it is greater than 1 mm, the component is considered to have failed in projection, and the rotation reference point of the source component is readjusted. The relative pose matrix of all successfully projected components is recorded and stored in JSON structure format for subsequent functional positioning. The functional action vector is called and combined with the three-dimensional projection result to generate a functional mapping matrix. The matrix establishes a one-to-one functional association relationship between each structural unit in the component unit and the corresponding connection point. The spatial center of gravity distance and the similarity of the functional vector are used as the main matching criteria in the mapping process, and the grid fragments inside each component unit are divided into the minimum functional area. , the K-means clustering algorithm is used to cluster geometric fragments of the same functional category and identify unique functional domain numbers. For example, the heating point areas corresponding to the light source components need to be clustered in the same number and mapped to the power control point or the heat conduction point. At the same time, the functional positioning is further refined through the electrical characteristics of the connection points, and the three-dimensional positions of the control interface, power supply interface and grounding port are marked. The attribute fields of the component units are updated through the functional mapping matrix to form clear functional partition labels and embedded in the corresponding triangular mesh structure to form the three-dimensional basic component units of the lamps after functional positioning. The three-dimensional Boolean operation and spatial voxel clipping modules are called to geometrically split the component unit model. First, the cutting boundary volume is generated according to the functional number identified in the functional mapping.This volume is a spatial convex hull structure based on the functional boundary. The original mesh model is then subjected to a Boolean clipping operation using this cutting boundary volume as a template. During the clipping process, Boolean difference and intersection operations are called to generate independent functional substructures. All divided substructures are named and classified according to their functional numbers. All optical-related functional substructures are uniformly classified into a 3D light source unit set, and the remaining functional substructures are classified into a 3D decoration unit set. A volume calculation operation is then performed on each segmented unit. The actual volume value of the unit component is calculated by subtracting the cavity volume from the volume of the external bounding box. The volume of the 3D decoration unit is limited to between 1 and 1000 cubic centimeters, and the volume of the 3D light source unit is limited to between 10 and 500 cubic centimeters. If it exceeds the limit, it is further divided into blocks through a re-segmentation algorithm. The final output of the spatial segmentation result is a 3D model set with functional labels, spatial coordinates, and volume parameters.

[0054] Preferably, in step S3, confirming the physical support structure of the lamp according to the three-dimensional decorative unit includes:

[0055] Identify the unit geometry of three-dimensional decorative units;

[0056] Fitting the cell geometry to the three-dimensional edge contours of the decoration cell;

[0057] Partially cutting the three-dimensional edge profile to obtain a decorative unit section frame;

[0058] Analyze the unit centroid data of the three-dimensional decoration unit according to the decoration unit section framework;

[0059] Extract material composition data of three-dimensional decorative units;

[0060] Perform material distribution simulation based on material composition data and the decorative unit cross-section framework to generate decorative unit material distribution data;

[0061] Couple the unit centroid data and the decoration unit material distribution data to infer the mechanical center of gravity of the decoration unit;

[0062] Gravity conduction tracking is performed through the mechanical center of gravity of the decorative unit to generate the force conduction path of the lamp;

[0063] Reconstruct the physical support structure of the lamp according to the force conduction path of the lamp.

[0064] In this embodiment, when identifying the unit geometric shape of the three-dimensional decorative unit, it is necessary to perform face clustering based on the three-dimensional triangular mesh model after preprocessing, and use the region growing algorithm to aggregate the face domains with continuous curvature. The initial face domain segmentation is achieved by setting the average curvature threshold between 0.015 and 0.03, and then the main direction vector analysis module is called to extract the main normal vector direction of each face domain, and the adjacent face domains are compared for similarity. If the main direction angle is less than 15 degrees, they are merged into the same geometric fragment. The merged face domain is then subjected to shape factor extraction processing, and parameters such as the main axis length, the maximum circumscribed sphere diameter, the average convexity index, the number of boundary polylines, and the number of face patches are calculated respectively. Finally, the identified geometric shape is identified as a convex sphere, a free-form surface, a rectangular parallelepiped surface, a rotation body surface, and other structural labels, and the geometric labels are embedded in the three In the model nodes of 3D decorative units, when fitting the unit geometry into the 3D edge contour of the decorative unit, a set of unit surface boundary points must be constructed first. By traversing the edge mesh of each face region, the endpoints of the edge segments are extracted to form a sparse boundary point set. The point set is then fitted with a minimum boundary curve. A continuous boundary curve is generated using Catmull-Rom interpolation spline. The connection relationship between the endpoints of each boundary curve is topologically closed to form a closed edge contour structure. The closed curve is then projected 3D onto the three principal planes (XY, YZ, and XZ). A multi-view contour set is established and fused into a spatial edge contour mesh according to the view reconstruction rule. The contour mesh is discretized using a dense curve. The number of edge control points is not less than 200, and the curvature difference between each control point is controlled to be 0.02, to ensure the spatial continuity and shape fidelity of edge fitting. When partially sectioning the 3D edge contour, first set the section direction vector perpendicular to the local surface according to the center point of the edge contour, and then construct a scanning section body with the section vector as the central axis. The scanning section body is a set of parallel planes with equal spacing, and the spacing is set to 1 mm to 3 mm. A section line segment is generated at the intersection of each section plane and the 3D edge contour. The section contour fragment is constructed through the control points on each line segment. All section contours are smoothed using the Bezier interpolation algorithm to finally generate a section frame. The section frame contains a hierarchical structure. The section profile of each layer maintains an interpolation consistency of 1 mm with the adjacent layers to ensure the continuity of the three-dimensional structure after sectioning. The output section frame is stored in OBJ format for subsequent structural analysis. When parsing the unit centroid data of the three-dimensional decorative unit according to the decorative unit section frame, it is necessary to first build a three-dimensional mesh model based on the section frame. Each section profile is swept and reconstructed to generate a volume block. The accuracy of the volume block is controlled to one voxel unit per cubic millimeter. The uniform mass distribution assumption is used for three-dimensional integral operation. The space of each voxel unit is The coordinates are averaged and weighted to obtain the spatial geometric centroid coordinates of the overall structure. The centroid coordinates are represented as a three-dimensional vector, representing the center of gravity of the position distribution along the X, Y, and Z axes, respectively. After the centroid calculation is completed, this data is injected into the attribute description node of the decorative unit for subsequent centroid modeling operations. To extract the material composition data of the three-dimensional decorative unit, an X-ray fluorescence spectrometer is used to perform non-contact analysis of the material information in the solid model or simulated material model. The test parameters are set as a voltage of 50kV, a current of 150μA, an exposure time of 120 seconds, and a scanning spot diameter of 1mm. After obtaining the elemental spectral data of the decorative unit shell and internal filling, the material database is used for element comparison and matching. The identified materials include ABS engineering plastics, PC polycarbonate, aluminum alloy 6061-T6, and stainless steel 304. The proportion of each material in the unit model is recorded, and the volume distribution ratio of different materials in space is expressed as a percentage. Based on the material composition data and the decorative unit cross-sectional framework, a spatial material distribution voxel grid is constructed for material distribution simulation, with a resolution of 0.5 mm. A cross-section framework defines the mesh boundaries. Material composition data is used as weighted input for the material fill tag of each voxel. A 3D Gaussian random field is used to simulate the material filling process. Spatial bias functions for different materials are set to control the material concentration areas. For example, ABS engineering plastic is concentrated in the outer layer of the cladding with a thickness of 2 mm, while aluminum alloy is used as the internal structural framework and distributed around the center of mass. A unique material tag is generated for each voxel, forming a complete material distribution map. The output format is a 3D matrix, with each coordinate point corresponding to a specific material type number. The unit center of mass data and the material distribution data of the decorative unit are coupled to infer the mechanical center of gravity of the decorative unit. The center of mass of each voxel is calculated based on the 3D material distribution voxel grid and a mass-weighted integral is performed. The mass calculation is based on the density parameter of each material, with ABS set to 1.04 g / cm. 3 , aluminum alloy is set to 2.7g / cm 3 , stainless steel is set to 7.9g / cm 3 , PC material is set to 1.2g / cm 3 , perform mass-weighted three-dimensional integration operations on all voxel units and map the results to a three-dimensional space center of gravity vector. This vector is the mechanical center of gravity position of the decoration unit. The generated mechanical center of gravity data structure is consistent with the center of mass data structure format. When tracing gravity conduction through the mechanical center of gravity of the decoration unit, construct a gravity vector field and project downward from the center of gravity to construct a gravity path. Set the gravity direction to the -Z axis direction and the unit gravity acceleration to 9.8m / s 2 , the three-dimensional material distribution grid is used as the path propagation medium, and the voxel force flow analysis algorithm is used to simulate the conduction path of gravity in each material unit. The material stiffness is considered in the path calculation. The stiffness of ABS is set to 2.1GPa and that of aluminum alloy is set to 70GPa. The stiffness affects the path offset. Each path node records the spatial coordinates, force conduction angle, force loss coefficient and the connection relationship with the next level path, and finally generates a complete force conduction path map. When reconstructing the physical support structure of the lamp according to the force conduction path of the lamp, the path termination point is identified and the path aggregation area is extracted as the physical support node, and each support node is further The shortest structural connection is made and a support skeleton model is constructed based on the topological structure. The support structure is composed of a combination of circular tube beam units. The diameter of the circular tube beam is 10 mm, and the material is structural steel Q235. The Young modulus is set to 200 GPa. The support structure layout must meet the structural stability conditions. Static simulation is performed on the reconstructed support structure, and the load is set to the sum of the gravity of the decorative units multiplied by a safety factor of 1.5. After the maximum displacement is evaluated to be less than 0.5 mm in the simulation, it is determined that the support structure is valid. The final output is a three-dimensional solid model file of the physical support structure of the lamp and imported into the subsequent simulation module to complete the structural integration modeling.

[0065] It is particularly important to couple the unit centroid data and the decoration unit material distribution data and infer the mechanical center of gravity of the decoration unit, including:

[0066] Perform spatial node pairing on the unit centroid data and the decoration unit material distribution data to obtain the centroid material node data;

[0067] Perform attribute weight fusion according to the centroid material node data to obtain weight fusion node data;

[0068] Perform spatial coupling mapping based on weighted fusion node data to obtain coupling distribution data;

[0069] Perform principal axis projection processing on the coupled distribution data to obtain principal axis projection data;

[0070] The center of gravity offset of the principal axis projection data is inferred to obtain the mechanical center of gravity data of the decoration unit.

[0071] In this embodiment, the unit centroid data is loaded into the node grid coordinate system in the form of a three-dimensional vector. Each centroid node coordinate contains X, Y, and Z axis values ​​in millimeters. At the same time, the decoration unit material distribution data is parsed into a voxel-level three-dimensional matrix structure. Each voxel unit contains spatial position coordinates, material number, and material density value. First, a spatial KD tree index structure is established to quickly retrieve the nearest neighbor position of the centroid node in the material voxel grid. Each centroid node is spatially overlapped with the material nodes in the surrounding 3×3×3 voxel area. If the centroid node is within 3 mm of the center of a material voxel, the material voxel is marked as overlapping with the centroid. Successful node pairing ultimately generates a set of centroid material node data. Each set of data contains a three-dimensional position vector, the corresponding material number, and local density information at that point. A set of attributes is defined for each centroid material node, including the node coordinates, material density, local material distribution gradient, and the structural stiffness index of the adjacent voxels. Structural stiffness is input using the Young's modulus of the material: 2.1 GPa for ABS, 1.2 GPa for PC, 70 GPa for aluminum alloy, and 195 GPa for stainless steel. After normalization, each attribute is weighted primarily by density, with a density weight of 0.5, a structural stiffness weight of 0.3, and a material gradient weight of 0.2. A linear weighted fusion formula is used to construct a single physical weight for each fusion node. The weight is combined with the corresponding spatial coordinate to form a weighted fusion node data set. The data set structure is a triple vector, including a spatial coordinate vector and a weight scalar, which is used in the subsequent spatial field modeling process to construct a three-dimensional coupling vector field. The vector field uses the boundary of the entire decoration unit as the coordinate system range, and all weighted fusion node coordinates are interpolated and embedded in the coordinate system. The interpolation method uses the Inverse Distance Weighting interpolation method (IDW, inverse distance weighted method), and the weight influence radius is set to 10 mm. For each spatial coordinate point, the weighted values ​​of the 8 weighted fusion nodes around it are calculated and a local field function is constructed. Each field function result is mapped to a coupling density value, which represents the spatial coupling strength of the coordinate point affected by the center of mass density and material stiffness. The coupling distribution data finally constructed is a three-dimensional continuous scalar field. Each coordinate point contains a coupling density value. The data structure is stored in the form of a dense voxel grid. The voxel accuracy is set to 1 mm, and the overall grid size is based on the boundary of the decoration unit. The box is automatically adjusted, and the three-dimensional coupling distribution data field is processed by PCA principal component analysis. The first principal axis direction of the distribution data is extracted as the main projection direction. The principal axis takes the maximum variance direction as the calculation basis, and then the entire three-dimensional coupling distribution data is orthogonally projected with the principal axis as the baseline. The projection result is a one-dimensional continuous vector density field. A density distribution function is constructed on the vector density field. The function takes the position in the principal axis direction as the independent variable and the coupling density value at the corresponding position as the dependent variable. The distribution mean of the coupling density on the principal axis is obtained by numerical integration. The mean position is the preliminary mechanical center trend point after projection. The maximum density concentration area and the boundary limit point are recorded for subsequent offset calculation. The main axis projection data records the density value of each main axis position point and its corresponding spatial coordinates in the form of an array. The position of the mass center point in the main axis direction is determined based on the projection density distribution function, that is, the main axis length is integrated by the density function, and the position of the integral result is determined by the density weighted average method as the initial center of gravity in the main axis direction. Then, the maximum density concentration area in the projection data is processed locally by second-order difference to identify the left and right density change rates. If the density increment on the left side is higher than that on the right side and exceeds the threshold value of 0.1, it means that the center of gravity is biased. The initial center of gravity position is corrected and offset along the principal axis. The offset is determined by the local density asymmetry function. The offset formula is Δx = α × (ρL-ρR) / (ρL+ρR), where ρL and ρR are the average density values ​​on the left and right sides of the center of gravity, respectively, and α is set to 0.02 of the principal axis length. The resulting offset correction is output as the mechanical center of gravity coordinates of the decoration unit. The mechanical center of gravity data structure is a three-dimensional vector corresponding to the mechanical equilibrium point position in the X, Y, and Z axes. It is stored in the data interface of the structural analysis module to facilitate the use of the gravity path analysis function in the subsequent simulation module.

[0072] Preferably, in step S3, performing weight aggravation simulation using basic data of the lighting equipment, and optimizing the load-bearing margin design of the physical support structure of the lighting equipment based on the simulated aggravated weight includes:

[0073] Get the weight data of the additional parts of the lamp;

[0074] Extract the overall weight data of the lamp from the basic data of the lamp equipment;

[0075] The weight data of the additional components of the lamp are used to simulate the weight superposition of the overall weight data of the lamp, thereby obtaining the simulated weight;

[0076] Conduct stress response simulation on the physical support structure of the lamp to generate the load-bearing capacity data of the lamp support structure;

[0077] Conduct interactive analysis based on simulated intensified weight and load-bearing capacity data to obtain the load-bearing requirement margin;

[0078] The load-bearing structure of the physical support structure of the lamp is optimized according to the load-bearing requirement margin to generate an optimized three-dimensional decorative unit.

[0079] In this embodiment, all non-core light source components in the three-dimensional decorative unit model are identified and numbered one by one. The identification process is classified and layered based on the component attribute tags in the model. All model nodes marked as "accessory structure" or "accessory decoration" will be extracted to establish a separate data layer. The geometric model of each additional component is calculated for volume through the three-dimensional CAD modeling interface. The tool used is the VolumeProperty function in the SolidWorks API interface. The volume unit is set to cubic millimeters. Then, according to the material label of the additional component, the corresponding material density value is read from the material database. The material database format is a CSV file, which includes parameters such as material number, density value, Young's modulus and Poisson's ratio. For example, the density of ABS is 1040 kilograms per cubic meter, the density of PC is 1210 kilograms per cubic meter, and the density of aluminum alloy is 2700 kilograms per cubic meter. The volume value is multiplied by the density to obtain the weight value of each additional component, and then the weights of all components are numerically superimposed to form the weight data of the lamp's additional components. It is stored in the form of a floating-point array, and each value represents the weight of a component in grams. The parameter configuration interface of the three-dimensional lamp basic model is called. The interface is defined in the extended attribute node of the STEP model file. The basic data field includes information such as the main structure size of the lamp body, material identification, design weight and center coordinate position. The original weight value of the entire lamp structure is obtained by parsing the "DesignWeight" field in the basic model. The weight value comes from the static load calibration data in the design stage and is stored in kilograms. The extraction operation of this field uses Python language combined with the openSTEP library for structural analysis. The structure field is screened for regular matching, and the numerical part of the matching result is converted into floating-point type and uniformly converted into grams and stored in the temporary buffer of the basic data file of the lighting equipment. The data obtained in this step will be used as the initial load of the simulation. All elements in the aforementioned additional component weight array are accumulated to form a total additional weight value, and then the whole lamp weight value in the basic data is numerically summed with the additional weight to form the simulated intensified weight. This calculation operation is performed using the array summation function and floating-point addition operator in the NumPy mathematical library. The simulated intensified weight data unit is grams, and the numerical accuracy is retained to two decimal places for subsequent application The force simulation loading provides a standard mass input value, and at the same time, the simulated intensified weight is embedded as a concentrated load into the mass point of the lamp model. The position of the mass point is provided by the coordinate value determined by the aforementioned mechanical center of gravity step. The specific coordinates are expressed in XYZ three-dimensional space values. The simulated intensified weight is applied to the node as a concentrated mass to participate in the structural force simulation. A finite element analysis model of the supporting structure is established, and HyperMesh is used to perform three-dimensional meshing on the structural model. The mesh unit type is set to tetrahedral unit, and the unit size is set to range from 2 mm to 4 mm. The local detail area adopts 2 mm dense division, and the coarse structure area adopts 4 mm division.Material properties are assigned according to the aforementioned material database. Each structural unit will be marked with a unique material number and attached with material physical parameters, including Young's modulus, Poisson's ratio, yield strength, etc. The simulation boundary conditions are set to completely fix the lower support surface, apply a load to the upper mechanical center of gravity node, and the load direction is the negative Z axis. The load value is equal to the gravity value corresponding to the simulated aggravated weight. The simulation software used is ANSYS The Mechanical module performs static analysis to obtain the stress value, displacement value and safety factor value of each structural node. The result data is exported in .vtk format and converted into a structural load-bearing capacity data set. The node stress value obtained by simulation is compared with the material yield limit of the structural unit. Matplotlib is used to draw the stress distribution diagram and superimpose the contour layer. Then, Pandas is used to construct a data table of the area where the stress exceeds the critical value. Each table item includes the unit number, maximum stress value, critical stress ratio and structural position coordinates. At the same time, the simulated intensified weight is proportionally mapped to each node to form an equivalent node load model. By comparing the actual load-bearing capacity of each node with the intensified weight distribution, a cross-analysis is performed to form the load-bearing margin data. The data is a safety factor distribution diagram under three-dimensional coordinates. The area with a safety factor less than 1 indicates The load-bearing capacity is insufficient. The area between 1 and 1.5 is a low safety margin area, and the area greater than 1.5 is a load-bearing area. All node data will be output in JSON structure format for optimization model input. The local optimization target domain is established based on the coordinate position of the low margin area. The shape optimization algorithm is used to adjust the geometric structure. The topology optimization tool in the OptiStruct module is used to construct the optimization objective function. The minimum structural mass is used as the constraint objective function, and the maximum stress of the structure does not exceed the material yield limit is used as the equality constraint. The material distribution morphology in the target area is controlled by the element removal rate. The number of optimization iterations is set to 150, the material removal threshold is set to 0.05, and the shape change accuracy is set to 0.1 mm. The residual downward trend is monitored during the optimization process to ensure optimization convergence. The optimized structural topology layout is finally obtained. The structure will be returned to the 3D modeling platform and the reconstruction modeling operation is completed through the CATIA V5 modeling interface. The retained area in the topology optimization result is converted into specific solid geometry. Rounded corners and assembly holes are added to form a new 3D decorative unit geometry model. The output format is .STEP standard model file and marked with the optimization version number.

[0080] It is particularly important to conduct interactive analysis based on simulated intensified weight and load-bearing capacity data to obtain the load-bearing requirement margin, including:

[0081] Perform node alignment processing on the simulated intensified weight and load-bearing capacity data to generate node weight comparison data;

[0082] Perform local load distribution screening based on node weight comparison data to obtain distribution screening data;

[0083] Mark the limit state of the distribution screening data and generate limit state data;

[0084] Calculate the bearing margin of the limit state data to obtain the bearing margin data;

[0085] The bearing margin data is integrated into the entire area to obtain the bearing requirement margin.

[0086] In this embodiment, the two types of data are normalized in three-dimensional spatial node coordinates. Specifically, the grid node coordinates of the physical support structure of the lamp are used as the spatial reference, and a three-dimensional point set alignment algorithm (Point Cloud Registration) is adopted. The ICP (Iterative Closest Point) algorithm is used to map the load-bearing capacity data to the simulated intensified weight loading points. The mapping operation must ensure that each loaded node maintains a maximum Euclidean distance of no more than 0.1 mm with the corresponding node in the load-bearing result. All node coordinate data are uniformly converted to millimeters and retained to three decimal places. A one-to-one correspondence between the intensified weight data and the stress-strain data is established through a node ID binding mechanism. A node weight comparison data structure is generated. Each node structure contains six fields: node number, three-dimensional coordinates, simulated weight, node stress value, material yield limit, and normalized weight ratio. The normalized weight ratio is the ratio of the node stress divided by the yield limit. The node group is clustered using a spatial region clustering algorithm. The clustering method adopts DBSCAN (Density-Based Spatial Clustering of Applications with The algorithm uses a density-based spatial clustering algorithm with a minimum neighborhood distance of 5 mm and a minimum number of cluster core points of 12. It identifies localized load anomalies within each cluster based on normalized weight ratios. If the number of nodes with a weight ratio greater than 0.8 exceeds 30% of the total number of nodes in a cluster, the region is marked as a high-load region. All nodes in each high-load region are renumbered and formed into independent data sets. Isolated data points and edge nodes are removed during the screening process to eliminate noise. The screening results are presented as high-load cluster numbers, number of nodes, average weight ratio, maximum weight ratio, and a spatial bounding box. This distributed screening dataset is then merged and stored in JSON format. The data structure retains all node numbers and weight ratios within each cluster. Each cluster in the screening data is individually extracted and subjected to limit conditions. The limit state is marked when the normalized weight ratio of the nodes exceeds 0.95, or the node stress value is greater than or equal to 95% of the material yield limit value. Nodes that meet any of these conditions will be marked as limit state nodes. All limit state nodes will be displayed as red nodes in the 3D visualization tool Paraview. Each limit state marked node will have additional fields to record its cluster number, local maximum stress, weight value corresponding to the loading point, and its position offset vector. All limit state nodes will be output in CSV form for bearing margin calculation. The table fields include: node ID, X coordinate, Y coordinate, Z coordinate, stress value, yield limit, normalized weight ratio, cluster area number, and limit mark state. The difference between the stress value of each limit node and its material yield limit is calculated to obtain the residual bearing capacity Δσ (delta sigma), then the unit weight projection component under the simulated exacerbated weight is applied to each node, and the load influence coefficient α of each node is calculated using the concentrated load distribution weight in the Z-axis direction. Then, the residual bearing capacity is divided by the load influence coefficient to form the local node bearing margin value, which is expressed in numerical form as follows: the bearing margin value is equal to the residual bearing capacity (unit MPa) divided by the load coefficient (unit Newton). The bearing margin values ​​of all nodes are aggregated to form a bearing margin data set. Each data record contains the node ID, cluster number, residual bearing capacity, load influence coefficient and bearing margin value. All cluster numbers are traversed, and the bearing margin values ​​in each cluster are weighted averaged according to the number of nodes to obtain the regional bearing margin value. Then, all cluster areas are divided according to their spatial positions. The structure was reorganized into a three-dimensional grid mapping structure for the complete support structure to form a spatial distribution map of the global load-bearing margin. Matplotlib and Mayavi libraries in Python were used to generate a three-dimensional color distribution map. The map used RGB color intensity to identify the local load-bearing margin level, with blue indicating a high margin and red indicating an approaching limit state. The load-bearing margin values ​​for each region were then averaged to generate global statistical indicators, including the average load-bearing margin, minimum load-bearing margin, maximum load-bearing margin, and the proportion of low-margin areas. Finally, all load-bearing margin statistics and the three-dimensional distribution map data were integrated to form a load-bearing requirement margin structure. The structure fields include the global average, the number of abnormal areas, the coordinates of the minimum load-bearing margin point, the percentage of low-margin nodes, and a list of recommended optimized area numbers.

[0087] Preferably, performing the lamp light scattering simulation based on the three-dimensional light source unit in step S4 includes:

[0088] Constructing a light source luminous domain based on a three-dimensional light source unit;

[0089] Perform light scattering node tracing based on the light source's luminous domain to generate multi-directional light scattering nodes;

[0090] Encoding the luminous intensity of the light source luminous domain to obtain the luminous intensity distribution code;

[0091] The luminous intensity distribution code is implanted into the multi-directional light scattering node to obtain simulated light scattering data.

[0092] In this embodiment, in the process of constructing the light source luminous domain based on the three-dimensional light source unit, it is necessary to rely on the geometric model of the lamp light source unit established in the three-dimensional modeling platform. The geometric model needs to include the light source shell structure, the light emitting surface position and the actual spatial coordinates of the internal light emitting core. By calling the light source construction function in the Photopia module in the optical simulation platform, the radiation morphological parameters of the light source contained in the light source model are read, wherein the radiation morphological parameters need to include the luminous flux unit (unit is lm), the luminous angle range (set in the horizontal and vertical angle directions respectively, in degrees), and the luminous starting plane (unit is mm). After completing the parameter configuration, the spatial grid algorithm based on the BSP (Binary Space Partition) tree structure is used to volumetrically segment the luminous envelope area of ​​the entire light source. During the segmentation, the segmentation spacing threshold needs to be set to be less than 5mm to ensure the high-density recording accuracy of the light source luminous path. Finally, all envelopes in the segmentation area are converted into a light source luminous domain voxel dataset. The dataset is saved in a structure format. Each voxel node records the luminous starting position, luminous direction vector, and spatial normal direction triple. By calling Monte The Carlo path tracing module uses the light source luminous domain voxel dataset as the starting path set, sets the total number of initial light emissions to 100,000, and uses an independent random ray generation algorithm for each path. The algorithm generates an initial direction vector for each voxel point, and its offset angle range is controlled within the maximum boundary of 0 to the luminous angle. The vector rotation matrix is ​​used to perturb the ray direction to ensure that the light is evenly distributed in different directions. During the path tracing process, the internal structural boundary model of the lamp needs to be loaded. The boundary model is composed of STL format and the BVH (Bounding Volume Hierarchy) algorithm is used to accelerate collision detection. The intersection of each light path and the boundary is detected. A scattering node is established for each intersection. The node records the incident direction, collision normal, refractive index, scattering angle, reflectivity and other parameters. Each light path generates no more than 15 scattering nodes. Finally, all scattering nodes are archived in the multi-directional light scattering node list according to the path number, and the light intensity distribution map data in the original lamp luminous parameters is called. This data is a luminous intensity matrix recorded in spherical coordinates, and its latitude dimension is a horizontal angle of 0 to 3 60 degrees, the longitude dimension is the vertical angle of 0 to 180 degrees, each direction node corresponds to a light intensity value, the unit is cd (candela), the data is converted into a three-dimensional light intensity tensor in a rectangular coordinate system, the dimension of the tensor is set to 100×100×100, and the corresponding spherical direction light intensity value is matched to each voxel position. The intermediate value is filled by the spherical interpolation algorithm to ensure that each voxel point has a continuous luminous intensity distribution value. The intensity value is then linearly normalized to keep its value range between 0 and 1, and then the normalized value is quantized using 8-bit binary encoding.Each voxel generates an 8-bit luminous intensity code. This luminous intensity code is embedded in the node data structure as additional information for each node in the light source's luminous domain, forming a luminous intensity distribution encoding matrix. Each scattering node in the multi-directional light scattering node list is matched. The voxel data in the light source's luminous domain is traced back based on the spatial coordinates of each scattering node and the direction vector of its previous node to identify its initial starting position. The 8-bit luminous intensity code in this starting voxel is used as the initial light intensity factor for the scattering node. This initial light intensity factor is then subjected to an attenuation function based on the reflectivity and scattering angle data of the scattering node. The light intensity attenuation function is set to I' = I0 × cosθ × R, where I0 is the original light intensity factor, θ is the scattering angle, and R is the reflectivity of the scattering surface. After all scattering nodes are corrected by this attenuation function, the additional field I' in their nodes is updated to form a complete list of simulated light scattering data. Each node in this list contains the spatial position, incident direction, exit direction, refractive index, scattering angle, reflectivity, and final light intensity value. This dataset satisfies the light scattering information configuration requirements of the light simulation module in the lighting simulation model.

[0093] Preferably, tracing the light scattering trajectory of the light source based on the simulated light scattering data in step S4 includes:

[0094] Mark the starting point of the simulated light scattering data to obtain the starting label of the scattered light;

[0095] Based on the scattered light starting tag structure light path vector, thereby generating the light path initial vector;

[0096] The energy transfer path is obtained by mapping the energy transfer between scattering nodes of the simulated light scattering data through the initial vector of the light path;

[0097] Perform path fusion and reorganization on the energy transfer path data to generate a fused scattering path;

[0098] The time series trajectory is reconstructed based on the fused scattering path to obtain the light source light scattering trajectory.

[0099] In this embodiment, a simulated light scattering data matrix containing the coordinates of each scattering node in three-dimensional space, the luminous angle and the unit energy density information is loaded. The data matrix is ​​generated by the previous simulation stage and stored in a structured manner as a multi-dimensional coordinate array. The coordinate constraints based on the three-dimensional Euclidean space are used to call a volume annotation algorithm for voxel tracking. The minimum containing sphere structure is established in the simulation space according to the geometric center of the light source unit. All photon units diverging outward from the surface of the structure are used as a reference. The spatial distance between the initial emission angle and the starting point is determined point by point to be lower than the set threshold d0, where d0 is 0.25 mm. Each node that meets the conditions is assigned an identification code tag_start through a custom annotation logic. At the same time, its corresponding emission direction vector and energy unit are bound to an independent structure field, thereby outputting a scattered light starting tag data file containing tag_start information and performing retrieval. The sorting process is introduced, and the starting label data output in the previous step is vectorized. First, a three-dimensional vector representation is constructed based on the coordinate field of each starting label node and its accompanying direction vector field, and the representation format is converted into a column vector matrix V_start, where each column represents the spatial emission direction of a light ray. Then, the coordinate position matrix C_start is weighted multiplied by V_start element by element, and the standard linear combination structure is used to calculate the displacement vector ΔX of each point in unit time t=1. At the same time, the energy attenuation rate η of each unit voxel penetration path of the light is set to set the initial energy E0=1w, and an initial vector structure set is constructed. Each element contains the starting coordinates, direction vector, unit step size, energy attenuation function, and current state identification field state_flag. Finally, the structure set is exported as the initial vector file of the light path and written into the simulation database in JSON format as the input source for subsequent path tracing, based on Monte The Carlo ray tracing method constructs an energy transfer mapping process between nodes. Each structure instance in the initial vector file is used as a particle source emission base point. By calling the path stepping function in the ray tracing module, light propagation is simulated in a three-dimensional voxel grid space. The propagation distance Δd is fixed at 0.1 mm, and the stepping is based on the light propagation direction vector. Simultaneously, at the current node, the simulated light scattering data is searched for matching nodes with overlapping coordinates or distances less than an error tolerance ε. If so, an energy transfer operation is performed, attenuating the remaining energy at the previous node by a proportional factor η and transferring it to the current node. The source node, target node, attenuation factor, and transferred energy are recorded, and a set of energy transfer event quads is constructed. Iterations are continued until the current energy value falls below the threshold E_min = 0.01w or reaches the maximum number of jump steps N_max = 100, all energy transfer events are stored as a directed graph structure, with nodes as vertices and energy transfer paths as edges, and a complete energy transfer path graph G_energy is output. After the construction is completed, an index file is generated for path fusion. All directed path sets with the same starting point tag_start as the source point are extracted from the G_energy graph exported in the previous stage, and the weighted aggregation algorithm is used to merge the homologous multi-branch paths. The Dijkstra shortest weighted path algorithm is used as the core mechanism of path fusion, where the edge weight is the transmission energy after unit energy attenuation. The fused path must meet the cumulative energy of not less than the set critical threshold E_thresh = 0.05w. After path fusion, an aggregated path structure is constructed for each group of light source nodes, all nodes in the path are sequentially spliced ​​with the direction vector, and the tail fragment path segments with energy less than E_drop = 0.005w are removed. Finally, a serialized data structure containing each fused path is output. The data structure contains the path number, Key fields such as the path node sequence, direction sequence, accumulated energy sequence, and path hop count are imported into the trajectory reconstruction module through a unified data interface as input parameters. The node sequence and direction vector sequence in the fused path structure are used to perform equally spaced temporal interpolation using the path interpolation function, with the time step t_step set to 1 millisecond. The total path length L_path is first calculated, and the number of interpolation points N_interp is determined based on L_path and t_step. A three-dimensional vector linear interpolation algorithm is used to perform temporal interpolation on the original path nodes. Each interpolation node contains spatial coordinates, direction vectors, timestamps, and energy value fields. A forward differential velocity estimation model is then introduced to continuously model the path change trend. A fifth-order B-spline fitting algorithm is used to smooth the temporal trajectory. The constructed temporal trajectory structure contains the path ID, start time, end time, path point sequence, fitting residual mean, and total energy value. Finally, the light scattering trajectory of the light source is stored in the simulation results directory in HDF5 format.

[0100] Preferably, in step S5, optimizing the light source influence on the optimized three-dimensional decorative unit according to the light source light scattering trajectory includes:

[0101] Combine the light scattering trajectory of the light source and optimize the three-dimensional decorative units and project them into the same space to obtain a combined projection frame;

[0102] Based on the combined projection framework, the light scattering trajectory of the light source is identified and the projection overlap area of ​​the three-dimensional decorative unit is optimized;

[0103] Optimize the light-blocking decoration area in the three-dimensional decoration unit by mapping the projected overlapping area;

[0104] Performing decoration optimization reconstruction on the optimized three-dimensional decoration unit according to the light-blocking decoration area to obtain a candidate reconstructed three-dimensional decoration unit;

[0105] A load-bearing capacity self-inspection is performed based on the candidate reconstructed three-dimensional decorative units, and based on the self-inspected load-bearing capacity, the candidate reconstructed three-dimensional decorative units are screened for load-bearing qualifications to obtain a final three-dimensional decorative unit.

[0106] In this embodiment, light source light scattering trajectory data is imported, which includes a spatial coordinate sequence, a direction vector, and a timestamp information. The trajectory points are normalized according to a unified three-dimensional coordinate system, and the trajectory coordinates are adjusted to the local coordinate system of the decorative unit in the simulation space. The coordinate system conversion is completed using a three-dimensional geometric transformation matrix, including a translation matrix and a plane rotation matrix, to ensure that the trajectory and the vertex coordinates of the three-dimensional decorative unit model are in the same coordinate system. The trajectory points and the surface vertices of the decorative unit model are orthogonally projected or perspectively projected using a projection matrix to construct a combined projection framework. The framework uses the three-dimensional space as a base to generate a hybrid data structure including a trajectory point set and a decorative unit facet set, which is specifically composed of point cloud data and polygon mesh data. The structure is imported into a graphics processing unit (GPU) for parallel computing optimization, and finally a high-precision projection matching framework file is generated. The spatial overlap detection algorithm is called to perform pixel-by-pixel comparison on the projection image of the trajectory point set and the decorative unit polygon mesh. The three-dimensional trajectory and the decorative unit model are rendered into two-dimensional projection images respectively using rasterization technology, and a pixel-level occlusion culling method is used. Invalid points are filtered out. By calculating the intersection of the projection of the trajectory point and the overlapping area of ​​the projection of the decoration unit, the probability density of the overlapping pixel area is estimated using the Monte Carlo integration method based on two-dimensional space. The overlapping points with inconsistent depth are excluded in combination with the depth buffer (Z-buffer) information, and the accurate identification of the intersection volume of the light source trajectory and the decoration unit in three-dimensional space is realized. The boundary polygon set of the overlapping area and the corresponding spatial index data structure are output and stored as the projection overlapping area annotation file. The boundary polygon information of the projection overlapping area is mapped back to the three-dimensional decoration unit surface. The two-dimensional projection coordinates are converted into three-dimensional surface coordinates using the inverse projection algorithm. The vertices in the mapping area are filtered in combination with the vertex normal vector information of the decoration unit mesh. The screening conditions are that the vertex position must fall within the polygon of the overlapping area and the angle between its normal vector direction and the light source direction is no more than 30 degrees. The local surface curvature and material properties of the selected vertex are calculated, and a region partition model based on vertex weight is established. The vertices that meet the conditions are classified as light-blocking decoration areas and their attributes are marked as high light blocking properties. The light-blocking area boundary is refined using a Cut algorithm to ensure smooth edges. Finally, a model data file of the light-blocking decorative area is generated, containing vertex indices, attribute labels, and boundary description parameters. Finite element analysis (FEA)-based structural optimization tools are used to subdivide the light-blocking decorative area, with the subdivided mesh edge length set to 0.5 mm, build a multi-layer grid level, define the material elastic modulus and density parameters for each subdivision unit, input the load boundary conditions, and convert the load into an equivalent thermal load distribution by the thermal radiation intensity generated by the light scattering trajectory of the light source. Use the topology optimization algorithm to adjust the material distribution in the light-blocking area. The optimization objective function includes the minimum material utilization rate and the maximum shading efficiency. Through iterative calculation, the grid structure of the light-blocking area is automatically reconstructed to form a candidate three-dimensional decorative unit model that meets the dual requirements of load-bearing and optical performance. The model file uses a high-precision grid file format (such as STL or OBJ), with detailed grid node coordinates and unit connection relationships. Load the high-precision grid model of the candidate reconstructed three-dimensional decorative unit. Based on the material's mechanical properties, the structural mechanics analysis module was invoked, and the finite element method was used to apply gravity loads and external environmental mechanical loads to the reconstructed elements. The load boundary conditions included a gravitational acceleration of 9.81 meters per second squared, with the load application point covering the maximum external extension of the decorative element. The stress and strain distribution within the element was calculated, and the Von Mises stress criterion was applied to assess structural safety. The structural safety margin threshold was set to 1.5 times the ultimate strength. Areas of maximum stress concentration were automatically identified, and a safety report was generated. All reconstructed elements that met the safety margin requirements were marked as qualified for load bearing. A batch processing system was then used to filter candidate models, ultimately outputting a set of 3D decorative element models that passed the load bearing screening.

[0107] Preferably, integrating the three-dimensional light source unit and the final three-dimensional decoration unit in step S5 includes:

[0108] Projecting the final three-dimensional decoration unit into a three-dimensional space to obtain a projected three-dimensional decoration unit;

[0109] Determining projection position data of the three-dimensional light source unit based on the physical connection relationship of the lighting equipment and the projected three-dimensional decorative unit;

[0110] The three-dimensional light source unit is projected into the projected three-dimensional decoration unit according to the projection position data, thereby being integrated into a lighting equipment design integrated model.

[0111] In this embodiment, high-precision model data of a three-dimensional decorative unit that has passed a load-bearing qualification screening is imported. This model data includes a vertex coordinate set, polygon mesh connection information, and material attribute tags. Based on the overall three-dimensional simulation space coordinate system of the lighting device, the decorative unit model is spatially positioned by constructing a transformation matrix. The specific transformations include a translation transformation matrix and a rotation transformation matrix. The translation vector is determined based on the coordinates of the decorative unit's design installation reference point, and the rotation angle is calculated based on the angular parameters of the decorative unit relative to the main structure of the lighting device in the design drawing. The transformation matrix is ​​applied to all vertex coordinates to complete the spatial repositioning of the model. Subsequently, a rendering engine generates a visual mesh structure of the projected three-dimensional decorative unit. This mesh structure file is stored in OBJ or FBX format and contains the projected vertex coordinates and updated facet indexes to ensure that all three-dimensional data is completely mapped to a unified space. The structural connection data of the lighting device is read, which includes the connection point coordinates, connection direction vectors, and connection types of each functional component. The spatial distribution relationship of the connection points is analyzed, and a relationship diagram between the connection points is established using a topological analysis method. Based on the spatial coordinate range of the projected three-dimensional decorative unit, the connection points preset in the light source unit in the connection relationship are accurately matched. A spatial constraint optimization algorithm is used. The method adjusts the position vector of the light source unit so that the spatial error between its connection point and the corresponding connection point of the decorative unit does not exceed 0.1 mm. The light source unit's posture is adjusted in combination with the rotation matrix to ensure that the connection direction conforms to the equipment design parameters. After the calculation is completed, a light source unit projection position data file is generated, including the light source unit coordinates, posture angle, and connection constraint parameters. The data is normalized using a unified coordinate system to ensure consistency with the spatial positioning of the projected three-dimensional decorative unit. A high-precision model file of the three-dimensional light source unit is loaded, which contains vertex data, polygonal patches, and light source material parameters. The vertex coordinates of the light source unit are converted to the unified three-dimensional simulation space of the lighting equipment through the spatial transformation matrix. Based on the coordinates and posture parameters in the projection position data, a complete rigid body transformation matrix is ​​constructed. The vertices of the light source unit model are transformed point by point to ensure that the spatial position of the light source unit model is completely aligned with the projected three-dimensional decorative unit. Subsequently, the possible geometric overlap areas on the surfaces of the light source unit and the decorative unit are calculated. The overlapping parts are fused using a Boolean operation algorithm to form a unified integrated model. Finally, a 3D model fusion tool is used to generate an integrated model of the lighting equipment design. The model file supports STEP or IGES formats and contains complete geometry, connection relationships, and material information.

[0112] The present invention further provides an integrated design system for a three-dimensional simulation model of a lighting device, which is used to execute the integrated design method for a three-dimensional simulation model of a lighting device as described above. The integrated design system for a three-dimensional simulation model of a lighting device includes:

[0113] The component modeling module is used to obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each component; and perform three-dimensional reconstruction of independent components based on the data of each component to obtain the three-dimensional basic lighting unit of each component;

[0114] A connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into a three-dimensional decorative unit and a three-dimensional light source unit based on the functional positioning of each unit;

[0115] The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit. It performs a weight-intensification simulation based on the basic data of the lamp equipment and optimizes the load-bearing margin of the lamp's physical support structure based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit.

[0116] A light simulation module is used to simulate the light scattering of lamps based on a three-dimensional light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data;

[0117] The fusion integration module is used to optimize the light source influence on the optimized three-dimensional decorative unit according to the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; integrating the three-dimensional light source unit and the final three-dimensional decorative unit to obtain an integrated model of lighting equipment design.

[0118] The present invention realizes the efficient acquisition and classification of basic data of lamp equipment through the implementation of component modeling module, ensures the systematicness and integrity of various component data, and the three-dimensional reconstruction of independent components provides intuitive visual expression, which is convenient for designers to conduct in-depth analysis of lamp structure. The introduction of connection identification module ensures that the physical connection relationship between the components of the lamp is accurately identified, thereby clearly defining the functional positioning of each unit. The division of three-dimensional decorative unit and three-dimensional light source unit provides a clear structural basis for subsequent design. The structural optimization module optimizes the load-bearing margin design of the physical support structure of the lamp through weight intensification simulation, ensuring the safety and stability of the lamp. The generated optimized three-dimensional decorative unit is not only improved in load-bearing capacity, but also enhances the overall aesthetics and practicality. The application of light simulation module simulates light scattering of three-dimensional light source unit. The generated simulated light scattering data provides an important empirical basis for the optical performance of the lamp. The process of tracing the light scattering trajectory of the light source provides accurate data support for the subsequent optimization of the light source influence. The implementation of the fusion integration module realizes the effective combination of optimizing the three-dimensional decorative unit and the light source influence. The final three-dimensional decorative unit generated improves the light efficiency and lighting effect of the lamp. The design integration model formed by integrating the three-dimensional light source unit and the final three-dimensional decorative unit not only ensures the consistency and coordination of the overall design, but also provides a detailed technical basis for actual production. The realization of the overall system improves the efficiency of lamp design, shortens the development cycle, reduces production costs, and at the same time promotes the digitalization and intelligentization of lamp design, meets the modern market demand for high-quality lamps, improves the market competitiveness and innovation ability of products, and injects new vitality and motivation into the development of the lamp industry.

[0119] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0120] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. An integrated design method for a three-dimensional simulation model of a lighting device, characterized in that: The following steps are involved: Step S1: Obtain basic data of lighting equipment; Classify the basic data of lighting equipment according to each component to obtain the data of each component; Perform 3D reconstruction of independent components through various component data to obtain the basic 3D lamp components of each component; Step S2: Obtain the physical connection relationship of the lighting equipment; Determine the functional positioning of each unit in the three-dimensional lamp basic component unit through the physical connection relationship of the lamp equipment; divide the three-dimensional lamp basic component unit into a three-dimensional decorative unit and a three-dimensional light source unit based on the functional positioning of each unit; Step S3: confirming the physical support structure of the lamp based on the three-dimensional decorative unit; performing a weight-intensified simulation using basic data of the lamp equipment, and optimizing the load-bearing margin of the physical support structure of the lamp based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit; Step S4: performing a light scattering simulation of the lamp based on the three-dimensional light source unit to generate simulated light scattering data; Tracing the light scattering trajectory of the light source based on the simulated light scattering data; Step S5: optimizing the light source influence on the optimized three-dimensional decoration unit according to the light source light scattering trajectory, thereby generating a final three-dimensional decoration unit; integrating the three-dimensional light source unit and the final three-dimensional decoration unit to obtain a lighting equipment design integrated model.

2. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire basic data of lighting equipment; perform multi-dimensional attribute annotation on the basic data of lighting equipment, and deconstruct the basic data of lighting equipment into structural hierarchies based on the attribute annotation data, thereby obtaining a component hierarchical relationship; Step S12: Mapping the component hierarchical relationship to the basic data of the lighting device, and classifying the basic data of the lighting device into component categories according to the component hierarchical relationship to obtain various types of component data, wherein the number of component categories is no less than four, including housing components, light source components, power supply components, and control components; Step S13: Perform three-dimensional projection of each classification using each component data to obtain a component framework of each classification, wherein the three-dimensional projection uses 0.1-1.0mm voxel accuracy for contour reconstruction; Step S14: Based on the component data, the component frames of each category are assembled into three-dimensional units to generate the three-dimensional lamp basic components of each component. The assembly size tolerance range is controlled within 0.3mm, and the component volume range is limited to 20-800cm 3 between.

3. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire the physical connection relationship of the lighting equipment; confirm the function of each connection point based on the physical connection relationship of the lighting equipment; Step S22: Virtually projecting the three-dimensional lamp basic component unit onto each lamp device point in the physical connection relationship of the lamp device, wherein the projection range is: projection angle less than 30°, 30° to 60°, and 60° to 90°, and the projection accuracy requires an error range of less than 1 mm; Step S23: mapping each lighting fixture point based on the functional role of each connection point, thereby obtaining the functional positioning of each unit in the three-dimensional lighting basic component unit; Step S24: Based on the functional positioning of each unit, the basic unit of the lighting device is spatially divided to obtain a three-dimensional decorative unit and a three-dimensional light source unit, wherein the volume range of each unit is 1-1000cm 3 , the volume of the three-dimensional light source unit is 10-500cm 3 .

4. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: Confirming the physical support structure of the lamp based on the three-dimensional decorative unit in step S3 includes: Identify the unit geometry of three-dimensional decorative units; Fitting the cell geometry to the three-dimensional edge contours of the decoration cell; Partially cutting the three-dimensional edge profile to obtain a decorative unit section frame; Analyze the unit centroid data of the three-dimensional decoration unit according to the decoration unit section framework; Extract material composition data of three-dimensional decorative units; Perform material distribution simulation based on material composition data and the decorative unit cross-section framework to generate decorative unit material distribution data; Couple the unit centroid data and the decoration unit material distribution data to infer the mechanical center of gravity of the decoration unit; Gravity conduction tracking is performed through the mechanical center of gravity of the decorative unit to generate the force conduction path of the lamp; Reconstruct the physical support structure of the lamp according to the force conduction path of the lamp.

5. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: In step S3, weight aggravation simulation is performed using basic data of the lighting equipment, and the load-bearing margin optimization design of the physical support structure of the lighting equipment is performed based on the simulated aggravated weight, including: Get the weight data of the additional parts of the lamp; Extract the overall weight data of the lamp from the basic data of the lamp equipment; The weight data of the additional components of the lamp are used to simulate the weight superposition of the overall weight data of the lamp, thereby obtaining the simulated weight; Conduct stress response simulation on the physical support structure of the lamp to generate the load-bearing capacity data of the lamp support structure; Conduct interactive analysis based on simulated intensified weight and load-bearing capacity data to obtain the load-bearing requirement margin; The load-bearing structure of the physical support structure of the lamp is optimized according to the load-bearing requirement margin to generate an optimized three-dimensional decorative unit.

6. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: The lamp light scattering simulation based on the three-dimensional light source unit in step S4 includes: Constructing a light source luminous domain based on a three-dimensional light source unit; Perform light scattering node tracing based on the light source's luminous domain to generate multi-directional light scattering nodes; Encoding the luminous intensity of the light source luminous domain to obtain the luminous intensity distribution code; The luminous intensity distribution code is implanted into the multi-directional light scattering node to obtain simulated light scattering data.

7. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: Tracing the light scattering trajectory of the light source based on the simulated light scattering data in step S4 includes: Mark the starting point of the simulated light scattering data to obtain the starting label of the scattered light; Based on the scattered light starting tag structure light path vector, thereby generating the light path initial vector; The energy transfer path is obtained by mapping the energy transfer between scattering nodes of the simulated light scattering data through the initial vector of the light path; Perform path fusion and reorganization on the energy transfer path data to generate a fused scattering path; The time series trajectory is reconstructed based on the fused scattering path to obtain the light source light scattering trajectory.

8. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: In step S5, optimizing the light source influence on the optimized three-dimensional decorative unit according to the light source light scattering trajectory includes: Combine the light scattering trajectory of the light source and optimize the three-dimensional decorative units and project them into the same space to obtain a combined projection frame; Based on the combined projection framework, the light scattering trajectory of the light source is identified and the projection overlap area of ​​the three-dimensional decorative unit is optimized; Optimize the light-blocking decoration area in the three-dimensional decoration unit by mapping the projected overlapping area; Performing decoration optimization reconstruction on the optimized three-dimensional decoration unit according to the light-blocking decoration area to obtain a candidate reconstructed three-dimensional decoration unit; A load-bearing capacity self-inspection is performed based on the candidate reconstructed three-dimensional decorative units, and based on the self-inspected load-bearing capacity, the candidate reconstructed three-dimensional decorative units are screened for load-bearing qualifications to obtain a final three-dimensional decorative unit.

9. The integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, characterized in that: In step S5, integrating the three-dimensional light source unit and the final three-dimensional decoration unit includes: Projecting the final three-dimensional decoration unit into a three-dimensional space to obtain a projected three-dimensional decoration unit; Determining projection position data of the three-dimensional light source unit based on the physical connection relationship of the lighting equipment and the projected three-dimensional decorative unit; The three-dimensional light source unit is projected into the projected three-dimensional decoration unit according to the projection position data, thereby being integrated into a lighting equipment design integrated model.

10. An integrated design system for a three-dimensional simulation model of a lighting device, characterized in that: For executing the integrated design method for a three-dimensional simulation model of a lighting device according to claim 1, the integrated design system for a three-dimensional simulation model of a lighting device comprises: The component modeling module is used to obtain basic data of lighting equipment; classify the basic data of lighting equipment according to each component to obtain data of each component; and perform three-dimensional reconstruction of independent components based on the data of each component to obtain the three-dimensional basic lighting unit of each component; A connection identification module is used to obtain the physical connection relationship of the lighting equipment; determine the functional positioning of each unit in the three-dimensional lighting basic component unit based on the physical connection relationship of the lighting equipment; and divide the three-dimensional lighting basic component unit into a three-dimensional decorative unit and a three-dimensional light source unit based on the functional positioning of each unit; The structural optimization module is used to confirm the physical support structure of the lamp based on the three-dimensional decorative unit. It performs a weight-intensification simulation based on the basic data of the lamp equipment and optimizes the load-bearing margin of the lamp's physical support structure based on the simulated intensified weight, thereby generating an optimized three-dimensional decorative unit. A light simulation module is used to simulate the light scattering of lamps based on a three-dimensional light source unit to generate simulated light scattering data; and to track the light scattering trajectory of the light source based on the simulated light scattering data; The fusion integration module is used to optimize the light source influence on the optimized three-dimensional decorative unit according to the light scattering trajectory of the light source, thereby generating the final three-dimensional decorative unit; integrating the three-dimensional light source unit and the final three-dimensional decorative unit to obtain an integrated model of lighting equipment design.

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